System and method for extracting physiological data using ultra-wideband radar and improved signal processing techniques

ABSTRACT

Disclosed is a variant of ultra-wide band (UWB) radar known as micropower impulse radar (MIR) combined with advanced signal processing techniques to provide a new type of medical imaging technology including frequency spectrum analysis and modern statistical filtering techniques to search for, acquire, track, or interrogate physiological data. Range gate settings are controlled to depths of interest within a patient and those settings are dynamically adjusted to optimize the physiological signals desired.

FIELD OF THE INVENTION

[0001] The field of this invention is medical diagnostic procedures and,in particular, quantitative measurements of physiological functions suchas, for example, heart and lung functions.

[0002] Disclosed is a variant of ultra-wide band (UWB) radar known asmicropower impulse radar (MIR) combined with modern signal processingtechniques to provide a new type of medical imaging technology.

BRIEF DESCRIPTION OF THE DRAWINGS

[0003]FIG. 1 is a general representation of the overall systemarchitecture useable in an embodiment of the invention.

[0004]FIG. 2 is a block diagram of a range delay circuit useable in anembodiment of the invention.

[0005]FIG. 3 is a block diagram of a balanced receiver useable in anembodiment of our invention.

[0006]FIG. 4 illustrates the general steps of the signal processing usedin an embodiment of our invention.

[0007] FIGS. 5(a) and 5(b) illustrate two versions of the time domainreturn signal matrix and their respective fill methods useful in anembodiment of the invention.

[0008]FIG. 6 illustrates general signal processing steps used in anembodiment of our invention.

[0009]FIG. 7 illustrates a frequency domain reflection signal matrixuseful in an embodiment of the invention.

[0010]FIG. 8 illustrates a Maximum Amplitude Frequency CoefficientVector.

[0011]FIG. 9 illustrates a block diagram of the Estimator of oneembodiment of our invention.

[0012]FIG. 10 illustrates a flow chart showing the modeler and theoperation of modeler signal processing useful in an embodiment of ourinvention.

[0013]FIG. 11 illustrates a flow chart of the correlator and correlatorsignal processing useful in an emobidment of our invention.

[0014]FIG. 12 illustrates a flow chart of the selector and selectorsignal processing useful in an embodiment of our invention.

[0015]FIG. 13 illustrates a flow chart of the analyzer and analyzersignal processing useful in an embodiment of our invention.

[0016]FIG. 14 illustrates a focuser feedback mechanism, and itsoperation, useful in an embodiment of our invention.

[0017]FIG. 15 illustrates a time domain return signal matrix with thedepth range of interest highlighted.

[0018]FIG. 16 is a sample depth range of interest indicator useful in anembodiment of our invention.

[0019]FIG. 17 is a screen shot of an application screen showing part ofa graphical user interface useful in one embodiment of our invention.

OVERVIEW

[0020]FIG. 1 shows a system diagram of an embodiment of our invention.In that figure, the controller 1 generates the timing and controlsignals 1 a, 1 b, 1 c 1 d, and 1 e to synchronize and manage the rest ofthe system. It also accepts internal feedback signals from the othersubsystems, accepts external control inputs from an operator, and hasthe capability of providing data outputs to the operator or medicalrecord system. The controller can be realized using an integratedprocessor and associated circuitry.

[0021] Based on timing and control signals 1 a from the controller 1,the pulse repetition frequency (PRF) generator 2 creates the basebandpulse train used by the transmitter 3 and, after range delay-ΔT 5, bythe receiver 6. Since the pulse train is common to both the transmitterand receiver subsystems and allows them to operate synchronously, thesystem is a time-coherent radar system. In practice, avoltage-controlled oscillator (VCO) operating at a nominal but onlyexemplary output frequency of 2 MHz in or associated with the PRFgenerator supplies the pulse train. Randomized pulse-to-pulse dither canbe added to the output of generator 2 by injecting a noise signal from anoise signal source (not shown) into the VCO control port. The randomdither causes spectral spreading to reduce the probability ofinterfering with other electronic devices as well as provide a uniquetransmit coding pattern per unit, allowing multiple units to operate inclose proximity without substantial concern for mutual interference.

[0022] Transmitter 3 generates a series of low-voltage, short-durationpulses 3 a (in one embodiment, less than 200 ps) based on the pulsetrain from the PRF generator 2. In practice, differentiating the edgesof a pulse train having extremely fast rising and falling edges createsthe sub-nanosecond pulses. Through the combination of the transmitterand the antenna, the short duration pulses are converted into anultra-wide band spectrum signal centered in the RF/microwave frequencybands in accordance with FCC R&O 02-48.

[0023] In this embodiment, the transmitter 3 and receiver 6 share acommon antenna though comparable designs could use separate antennas.For the transmitter, the antenna 4 couples the short pulses from thetransmitter 3 to the environment, as illustrated at 4 a, to patient 5.Subsequently, reflections 4 b are received from the environment and fedto the receiver 6. We have tested a variety of antennas ranging fromcommercially available horns and flat resonators to simple magneticdipoles. Based on empirical tests, a useful antenna proven to be amagnetic dipole or “loop” antenna with a diameter selected to optimizethe transmission and reception of UWB signals. This topology providesadequate gain, broad beam width, and small physical size. For example, aloop antenna with a diameter of 4 cm fabricated from 24-gauge solidcopper wire was used in conjunction with a UWB system operating with a10 dB bandwidth of 1.5 Ghz to 3.4 Ghz.

[0024] Based on timing and control signals 1 b from the controller 1 andthe pulses originating from the PRF generator 2, the range delay-ΔT 5generates a delayed version of the PRF timing signal. The output of therange delay triggers a sample-and-hold circuit, described subsequently,in the receiver 6 where the delay value is chosen to compensate forfixed electrical delays within the system and focus data collection tothose reflections originating from a specific depth within the body. Therange delay is extremely flexible and, in conjunction with thecontroller, can generate a large range of delay profiles to accommodatea variety of signal processing requirements.

[0025] There are two delay modes used to collect medical data—range gatemode and range finder mode. In range gate mode, the depth within thebody that corresponds to the area for which physiological data is to beextracted is fixed and a large number of samples are collected at thatdepth over a period of multiple seconds in one example, providinginformation on relative changes within the body. The depth can then bechanged and the process repeated. In contrast, when operating in rangefinder mode, the depth is swept repeatedly over a finite range ofinterest, with samples collected at each depth. Range gate mode providesdetailed information at the depth of interest while range finder mode isused to quickly collect data over a range of depths. The range delaycircuit of FIG. 2 supports both range gate and range finder modes. Inpractice, the range delay circuit can be realized using a 12-bitdigital-to-analog converter (DAC) 21, an operational amplifier, used torealize functions 23, 25, and 27, and a one-shot multivibrator 28. Theone-shot multivibrator (an LMC555 can be used, as one example) generatesa delayed version of the transmitted pulse train in response to signalsreceived on its two control inputs—trigger and hold-off. The pulse trainfrom the PRF generator 2 of FIG. 1 is the trigger signal and causes theone-shot multivibrator to initiate a single pulse cycle for each pulsein the pulse train. The hold-off voltage determines the period of thepulse. By varying the hold-off voltage, different pulse periods, andthus different delay values, can be generated. The amount of delay isset by both analog and digital controls. The analog controls set theminimum delay value and the allowable range of control while the digitalcontrols are used to dynamically adjust the actual delay value, delaysweep rate, and resolution of delay control.

[0026] In practice, a 12-bit data value—Data_(x), corresponding to thedesired delay is sent from the controller 1 to the DAC 21. The DACproduces a voltage V_(x) where:

V _(x)=4.096 Volts×(Data_(x)/4096)

[0027] The DAC output voltage 21 and a DC voltage 25 are added togetherin a summing junction 23 and the sum is amplified and fed to thehold-off control input of the one shot 28. The DC voltage level, inconjunction with the amplifier gain, set the minimum delay value and theallowable range of control. Both the DC voltage level and gain settingsare controlled by manual adjustment of potentiometers. A delay range of5 ns has been proven to yield good quantitative data in cardiopulmonaryapplications and corresponds to a depth range of approximately 12 cminto the body. Other delay range values of up to 10 ns have also shownto produce usable data sets.

[0028] The receiver 6 processes the raw reflections received from theantenna 4 over line 4 c in the analog domain to optimize the signals ofinterest. For cardiopulmonary data, this includes suppressing thehigh-strength static return signals and amplifying the motion artifacts.Receiver 6 is illustrated in detail in FIG. 3 and can be based on adual-channel balanced receiver architecture where the transmitter pulsesare capacitively coupled from the output of the transmitter 3 into bothreceive channels 30 a and 30 b via RF Splitter 30 and the antenna 4 isconnected or otherwise coupled to one channel 30 a. The balancedreceiver architecture provides a high degree of common mode rejection aswell as differential gain. The common mode rejection provides asignificant amount of attenuation to signals common to both channelsthus minimizing interference from the transmit signal with the desiredreceive signal. The differential gain inherent in this architectureamplifies signals unique to either channel thus the received signal,being unique to channel 30 a, is amplified.

[0029] Both channels 30 a, 30 b can use an ultra-fast sample-and-hold(S/H) circuit 32 a and 32 b each triggered by the delayed impulse traincreated by the pulse generator 31 using the delayed pulse train overline 29 from the range delay circuit-ΔT 5 of FIG. 1. The active samplingwindow is set at approximately 320 ps in one example and can be easilymodified by selectively changing the value of a single passivecomponent. The outputs of the two S/H circuits are integrated overmultiple samples in integrator elements 33 a and 33 b to improve thesignal-to-noise ratio. The integrated samples feed the inverting andnon-inverting inputs of an instrumentation amplifier 35, attenuating thetransmitted signal and amplifying the received signal.

[0030] Additional circuitry in the receiver incorporates several keypre-processing functions including static reflection suppression infilter 36, amplification in amplifier 37, and anti-alias filtering infilter 38. We have tested two basic techniques for suppression of staticreflections: feed-forward and feedback circuitry. In the feed-forwardcase, the output of the instrumentation amplifier 35 is applied to theinput of a low-pass filter (F_(cutoff)≦0.2 Hz) to attenuate frequencycomponents related to organ movement in the patient. The filtered signalis subtracted from the original output yielding a difference signal withreduced static reflection components. The difference signal is amplifiedto further enhance the desired signals with respect to the staticreflections. A diagram of the receiver with the lowpass feedback staticreflection filter 36 is shown in FIG. 3. In the feedback case, alow-pass filter 36 c is used as the feedback element. Again, the cornerfrequency of the filter is chosen to attenuate the motion artifacts. Theresulting signal out of the low pass filter primarily contains thestatic reflections, which are subtracted from the incoming signal. Thiscircuit significantly increases the signal-to-noise ratio for motionartifacts by attenuating the unwanted static return signals that wouldotherwise swamp out the motion components.

[0031] The amplification stage 37 is composed of two parts in ourexample,—range compensation and automatic gain control (AGC). The rangecompensated gain circuit compensates for received energy loss as thedistance from the antenna to the depth of interest increases byincreasing the gain over the delay sweep time. This block also includesselective blanking (38 dB of attenuation) capability to eliminateundesired reflections. It can be used, for example, to mask antennareflections due to impedance mismatch by attenuating all receivedsignals over a specific range. Range compensated gain can also bedisabled in favor of fixed gain. The AGC amplifies the small-signalradar return signals to achieve maximum dynamic range prior to thedigitization process, explained subsequently. The gain of theamplification stage has an adjustment range of −38 dB to +38 dB.

[0032] The final preprocessing stage includes the anti-aliasingcircuitry 38. The preprocessor low-pass filters the optimized data tominimize the potential of aliasing at the digitization stage. Asindicated by accepted signal processing techniques, the pass-bandcharacteristics, bandwidth, and order of the low-pass filter areselected to attenuate those frequency components at or above half thesample frequency (i.e., Nyquist frequency) to below the resolution ofthe analog-to-digital converter while minimizing distortion of thedesired frequency components, as is well known in the art. For example,the variables in the anti-alias filter design problem are the 3 dBcut-off frequency of the filter (f_(LPF)), the order of the filter (n),and the sample frequency (f_(S)). Usually the 3 dB cut-off frequency ofthe filter is selected to provide minimal attenuation of the desiredfrequency components related to the physiological event. It may also beset as low as possible or appropriate to reduce the corresponding samplerates in the digitization process thereby avoiding large amounts ofredundant data. With the 3 dB cut-off frequency of the filterdetermined, it is up to the designer to evaluate the interdependencebetween the filter order and sample frequency and arrive at acceptablevalues for each.

[0033] As an illustrative example of the anti-alias filter designprocess, the human cardiopulmonary frequency spectrum is band-limited toless than 5 Hz, corresponding to a maximum cardiac rate of 300 beats perminute. Basing the design around a 16 bit analog-to-digital converterhaving 65,536, that is, (2¹⁶), possible output states, the attenuationat the Nyquist frequency is calculated by:

Attenuation at f _(Nyquist)≧20 log₁₀(65,536)=96.33 dB; where: f_(Nyquist) ≦f _(sample)/2

[0034] Settling the 3 dB cut-off frequency of the filter at twice thehighest frequency component in the signal of interest to minimizedistortion and based on the approximation of 6 dB of attenuation perfilter pole yields the following table: TABLE 1 Attenuation versusNumber of Filter Poles Attenuation at 9600 Hz Number of (4 octaves above600 Hz @ Poles 24 dB/pole) 2 48 dB 3 72 dB 4 96 dB 5 120 dB 

[0035] In this case, a fourth-order low-pass filter with a 3 dB cut-offfrequency of 600 Hz will provide approximately 96 dB of attenuation forfrequencies above 9600 Hz. The corresponding sample rate can be set toany convenient value greater than 19200 Hz (2×9600 Hz). Other filterscan be employed to support a variety of sampling schemes.

[0036] As illustrated in FIG. 1, the A/D converter 7 (ADC) is controlledby Controller 1 through control lines 1 c. The controller sets thesample rate, sample resolution, and start/stop timing for the samplingprocess based on the mode of operation. The ADC digitizes the enhancedanalog motion reflections from the receiver 6, as described with respectto FIG. 3, translating the enhanced reflected energy into a series ofdiscrete digital values. As one example in range gate mode, we have used16,000 samples per second at 16-bits per sample.

[0037] The digitized signal from the A/D converter 7 is then processedto extract pertinent physiological information in signal processor 8 perFIG. 1. The signal processing block is extremely flexible and, asmentioned previously, can accommodate a wide variety of algorithms insupport of different medical applications. In addition the algorithm canbe implemented using parallel, serial, or hybrid parallel/serialarchitecture. The choice of a specific architecture is left to thoseskilled in the art and will depend on the application and other systemconstraints. The controller manages the signal processing operationsthrough control path 1 d.

[0038] The resultant physiological data is displayed on the UserInterface 9 (UI) of FIG. 1. This can include tracings of amplitudeversus time for one or more depths of interest, power spectral densityfor one or more depths of interest, time domain and frequency domainhistograms for a range of depths, numerical values for heart and/or lungrates, as well as the associated confidence factors for the displayeddata, as described subsequently. The Controller 1 of FIG. 1 converts thedata from the signal processor to an operator-friendly format throughcontrol path 1 e for display on the UI.

[0039] Signal Processing

[0040] The signal processing block 8 of FIG. 1 can comprise the threeblocks shown in FIG. 4. The three blocks of FIG. 4 can be implementedentirely in software on the Signal Processing block 8 of FIG. 1 in ourembodiment. Other implementations of these signal processing techniquesand their location within the system can be made without departing fromthe spirit or scope of the invention. The Extractor block converts thedigitized MIR reflections into a variety of useful physiological dataincluding cardiopulmonary data such as cardiac and pulmonary rate andrhythm (i.e., trending). The Analyzer block processes the time-orderedsequence of values and confidences measures from the Extractor andsearches for problematic trends in the values. The Focuser block is acontrol process that uses the results from one or more of the device'sstages to modify the amount and types of processing performed on eachpass through the system.

[0041] The input to the signal processing stage is the preprocessed anddigitized reflections produced by the ADC. The signal processor storesthe ADC output in a two-dimensional matrix of time-sampled reflectionvalues to support subsequent operations. In both FIGS. 5a and 5 b, thetime domain reflection matrix is organized with the columns contain datacollected at a specific sample interval with respect to thesynchronization signal and the rows contain data collected at a fixeddepth. The order of the digitized values depends on the mode ofoperation—i.e. range gate or range finder. For range gate mode asillustrated in FIG. 5a, the digitized values are organized as a seriesof contiguous values obtained at a fixed depth, providing information onrelative changes within the body for a specific depth. When the depth orrange gate setting is changed, a new series of contiguous values isproduced. For range finder mode as illustrated in FIG. 5b, the digitizedvalues are organized as a series of values obtained for a monotonicallyincreasing depth or range. A new series is generated for each sweepthrough the depth or range of interest. The matrix could bethree-dimensional if more than one MIR device is used simultaneously.For example, two synchronized MIR devices positioned at two differentpoints on a patient's chest.

[0042] Extractor

[0043] The Extractor 44 of FIG. 4 operates on the time domain reflectionmatrix to extract a variety of useful physiological data includingcardiopulmonary data such as cardiac and pulmonary rate and rhythm(i.e., trending). It is also extensible to measurements of many otherphysiological data collection applications including measurement ofparameters associated with cardiac chamber volume—e.g. stroke volume,ejection fraction, cardiac output, and the like. The Extractor 44 canoperate on either data files collected at an earlier time or continuousdata captured in real-time. It utilizes one or more control loops thatcan restrict the incoming data to a particular area for improvedcomputing efficiency or enhanced detail extraction. In one embodiment,the Extractor 44 is implemented entirely in software that runs on theSignal Processor block 8 of FIG. 1.

[0044]FIG. 6 shows the processing steps of the extractor and the datastructures produced by each step. In the preferred embodiment, weelected to implement a serial architecture because we were interested incollection of a single type of physiological data. The extractor couldhave been realized by a parallel design. A parallel design might beappropriate for those applications using multiple MIR devices,collecting a variety of data types, or requiring a variety of filtermodels. The following subsections describe the purpose of eachprocessing step in the extractor.

[0045] Reducer

[0046] The Reducer 71 of FIG. 6 is the first stage in the Extractor. Itreceives the preprocessed and digitized reflections produced by the ADCstored in the time domain reflection matrix 70. The operations (eitherone-dimensional or two-dimensional) performed in this stage furtherrefine the data to optimize specific parameters. For example, in thoseapplications where detection of movement is desired such as cardiacrate, a helpful operation involves reducing the contribution of staticreflections. The static reflections are attenuated by subtracting thetime domain date from an average. The average used for the differencingcan be as simple as the first stored row in the time domain reflectionmatrix to an actual average calculated from multiple rows in the matrix.In practice, we have found that an average calculated from 8 rowsprovides sufficient attenuation of the static reflections to enableaccurate detection of cardiac movement.

[0047] Other operations can include sub-sampling (with or withoutinterpolation) to reduce the volume of time-sampled data, coarserquantization to increase contrast and reduce computational complexity,and normalization to maximize dynamic range. In addition, some of thedata rows in the time domain reflection matrix may be deleted if theFocuser feedback mechanism 48 of FIG. 4 determines that reflections fromthose depths do not contain useful data or improve the quality of themeasurements. The Focuser is described subsequently. The output of thereducer stage is a second two-dimensional matrix containing enhancedtime-sampled reflection data 71 a shown in FIG. 6.

[0048] Transformer

[0049] The transformer step 72 of FIG. 6 converts the enhancedtime-sampled reflection data produced by the reducer to the frequencydomain for spectral processing. The following table lists several commontechniques typically used to implement the transform.

[0050] Discrete Fourier Transform (DFT)—a complex time-to-frequencydomain transformation most commonly implemented using a Fast FourierTransformation algorithm.

[0051] Discrete Cosine Transform (DCT)—similar to the DFT, the DCT is areal time-to-frequency domain transformation used extensively image andvideo compression.

[0052] Discrete Filter Bank (DFB)—a set of bandpass filters where theindividual pass bands are selected to separate the signal into specificfrequency ranges of interest.

[0053] The actual transform used will depend on the application andsystem processing capabilities.

[0054] For example, an inexpensive device used to collect basic cardiacrate and rhythm data for physical conditioning might employ the DCTwhile a more life-critical device for monitoring the condition ofindividuals suffering from coronary heart disease might require theadded precision of the FFT. A system using the DFB would provide amethod for determining when a specific physiological parameter deviatesfrom an acceptable range.

[0055] The transformed reflections are still in a 2-dimensional matrix72 a, but the time dimension has been translated into frequency and thevalue in each cell corresponds to the transform coefficient at a givendepth. In the case of a real transform—e.g. the DCT or DFB, each cellwill contain a single coefficient that corresponds to the amplitude ofthe energy contained in the original time domain signal for the givenfrequency. If a complex transform—i.e. an FFT, is employed, the value ineach cell will be complex coefficient, having a real and imaginarycomponent that may be converted to magnitude and phase through standardtrigonometric identities. FIG. 7 shows the frequency domain reflectionmatrix format where one axis corresponds to the depth of the samplewhile the other axis corresponds to the frequency. The frequency domainreflection matrix is used in subsequent phases of the Extractor.

[0056] Estimator

[0057] As detailed in FIG. 9, the Estimator 73 of FIG. 6 operates on thefrequency domain reflection matrix 72 a of FIG. 6 to derive anapproximate value for the physiological process under investigation foreach depth of study so that suitable models can be selected andoptimized in the next step (the Modeler 74 of FIG. 6). In addition, theapproximate values are forwarded to the “Selector” step 78, which willultimately determine the optimal measurement. The Estimator, Modeler andSelector can be implemented entirely in software running on the SignalProcessor 8 of FIG. 1.

[0058] To derive the above approximate values, the estimator creates atwo-dimensional vector 73 a of FIG. 6, as shown in detail in FIG. 8 oflength equal to the number of depths under investigation.

[0059] The values stored in the vector for each depth is equal to thefrequency having the highest amplitude coefficient at that depth and theamplitude of the coefficient. The pseudo-code below illustrates a sample“maximum value” search algorithm used to find the maximum amplitudecoefficient and its corresponding frequency for each depth underinvestigation. Last, this vector may be filtered to remove thosefrequency values that are outside the range of nominal or expectedvalues for each process under investigation. For example, in theinvestigation of cardiac rate, only those frequency values between 0.5Hz and 5 Hz are of interest (corresponding to 30 beats per minute to 300beats per minute). All values outside this range can be eliminated. Theuse of a post-estimator filtering step operates only on the estimationvector and preserves the original frequency domain reflection matrix sothat multiple physiological processes can be investigated simultaneouslyon the same data set. The filtered vector from the estimator isavailable for subsequent operations.

[0060] Sample Pseudo-code for Identifying Maximum Amplitude FrequencyCoefficient. For j = 1 to M ;Loop over all depths Let Max(j) = C(1,j);For depth “j”, initialize max amplitude to first amplitude Let Freq(j)= 1 ;For depth “j”, initialize frequency of max amplitude to 1^(st)freq. For l = 2 to N ;Loop over all frequencies at depth “j” If C(i,j) >Max(j) ;Search for and store max amplitude and corresponding freq. Then{ Set Max(j) = C(i,j) And Set Freq(j) = I } Else Next i ;Repeat acrossall frequencies Next j ;Repeat across all depths

[0061] This process is extensible to generating frequency signaturesthrough more sophisticated sorting of the frequency data and calculatingstatistics on the distribution of the coefficients. For example, oncethe maximum frequency coefficient is known, one could gain furtherinformation about the process under investigation through calculation ofthe standard deviation of the variation in amplitudes of the otherfrequency coefficient from the maximum. In addition, more than onephysiological process can be estimated on each pass. In that instance,multiple filtered vectors would be produced, one for each physiologicalprocess. For example, estimates for cardiac and pulmonary rates could begenerated from the same underlying frequency data.

[0062] Modeler

[0063] For each estimate vector produced by the Estimator 73 of FIG. 6,the Modeler 74 adapts one or more matched filters from its Filter Modeldatabase 75 into one or more vectors of matched filters 74 a forsubsequent cross-correlation with the original time-based reflectiondata. The database contains one or more matched filters that weredeveloped from a single cycle of the physiological process underinvestigation. An individual matched filter 75 a is developed byself-convolving a single cycle pattern to produce a matched filter forthat pattern. The formula used to create a matched filter—designated byMF(n), from a discrete pattern of length N—designated by P(n), using thediscrete form of convolution is:${{MF}(n)} = {\sum\limits_{m = 0}^{m = N}{{P(m)}{P\left( {n - m} \right)}}}$

[0064] The single cycle patterns used to generate the matched filterscan be based on simple periodic waveforms (e.g., a half-cycle sinusoid),more complex patterns developed through theoretical studies of expectedreflections, or actual captured patterns from individual patients. Thedatabase can include filters representing normal cycles as well asabnormal cycles resulting from a variety of ailments. The input patternscould be captured as part of a fitting or calibration process. Theactual selection of filters used to populate the database is expected tobe application dependent.

[0065] As an example, for a system that measures cardiac rate andrhythm, the database would contain filters based on various singlecardiac cycles. The filters in the database may include entries based ona half-cycle sinusoid, ideal normal patterns, patterns captured from apatient, and abnormal patterns corresponding to bradycardia,tachycardia, and fibrillation. The application of multiple filtersallows the system to select the filter that better “matches” theincoming reflections, improving tracking of the process underobservation and supporting identification of normal and abnormalpatterns. The process of evaluating the degree of “match” and filterselection is handled by subsequent operations.

[0066] With a database containing one or more matched filters, theModeler 74 uses the frequency coefficient vector from the Estimator 73to generate a first-order estimate of the cardiac rate for each depth inthe vector and adapts the filters in its database to the estimated ratethrough expansion or contraction of the period of the single cyclemodels. This can be looked upon as customization. For example, supposethe matched filter is based on a half cycle sinusoid with a nominalperiod of 1 second (equivalent to 60 beats per minute) and the estimatefor depth N is 0.75 seconds (equivalent to 80 beats per minute). Theoriginal matched filter stored in the database would have a period of 2seconds since it is twice the length of the pattern used in the selfconvolution and the adapted filter would have a period of 1.5 seconds.As the Estimator is enhanced to produce more sophisticated estimates,the Modeler can be modified to support adjustment of additional matchedfilter parameters. For example, as the resolution of the underlyingsystem is increased, it may be possible to differentiate between atrialand ventricular activity. The timing between these two types of eventsmay prove to vary for different individuals or medical conditions.Adaptation of the matched filter to account for variations in bothoverall period as well as atrial/ventricular spacing may provide moreaccurate or medically significant data.

[0067] Feedback from subsequent steps in the overall algorithm mayeliminate some matched filters from consideration because themeasurements derived from them are of consistently poor quality comparedto other models. For example, if the modeler is using a sinusoid and asquare wave as the basis for its two matched filters and it isdetermined that the filter derived from the square wave consistentlygives poor results (i.e., unrealistic measurements) compared to thesinusoid, the square wave model will be dropped from consideration.Conversely if the system had previously reduced the number of filtertypes to a single model and the quality of the data began to degrade,the system may decide to apply multiple models in an attempt to find abetter match. This feedback mechanism is handled by the Focuser 48 ofFIG. 4.

[0068] In the implementation shown in FIG. 9, the modeler is realizedusing a serial architecture requiring multiple passes to complete allcalculations. It may model more than one physiological process perinvocation. In block 114, the estimates for the i-th physiologicalprocess being measured are received from the Estimator 73. In block 112,matched filter(s) are selected from the database of filters 113.Feedback from the Focuser 48 on which filters have been providing thebest results is provided through block 111. Block 112 may use thisfeedback to eliminate one or more filters from consideration. Once theset of filters (one or more) have been chosen, they are each customizedin block 114 based on the estimates received from the Estimator.Customization is discussed more fully two paragraphs above. Decisionblock 115 causes each filter to be customized in turn until they are alldone. At that point, the customized filters are forwarded to theCorrelator 77 in block 116. These steps are run for each physiologicalprocess being measured. For example, given estimate vectors for cardiacand pulmonary rates, it would produce a set of matched filters for bothprocesses where each filter set may contain one or more filter typeswith each type adapted for every depth represented in the estimatevector.

[0069] Correlator

[0070] The Correlator 76 of FIG. 6 utilizes the concept of matchedfilters and correlation to calculate a matrix of correlationcoefficients for the measurements of the physiological process underinvestigation. The Correlator can be implemented entirely in softwarerunning on the Signal Processor 8, if desired. The vector, or set, ofadapted filter models 74 a from the Modeler 74 is cross-correlated withthe enhanced time-based reflection matrix 71 a from the Reducer 71 toproduce a series of correlation coefficient matrices with one matrix permatched filter model vector. The cross-correlation of a single matchedfilter MF(n) of length N and the data from depth D_(i) of length M isgiven by the following formula:${{R_{{MF},D_{i}}(p)} = {\sum\limits_{m = 0}^{m = M}{{{MF}(m)}{D_{i}\left( {p + m} \right)}}}};{{\text{for}\quad p} \Subset \left( {{- N},M} \right)}$

[0071] The Correlator may receive more than one customized model foreach physiological process being measured. FIG. 11 illustrates theprocessing blocks in the Correlator for one filter model applied againstone physiological process. In block 130, the i-th model for the j-thphysiological process is received from the Modeler 75. That model iscross-correlated in block 131 with the enhanced time-based signals. Thatis, the i-th model for the j-th physiological process at the k-th depthis cross-correlated with the enhanced time-domain data for depth k.

[0072] AC Peak Detector

[0073] The AC peak detector 77 shown in FIG. 6 operates on each row ofthe correlation coefficient matrix 76 a from the Correlator 76. Each rowin the correlation matrix is DC-filtered to remove any common bias. Theresultant filtered rows are run through a peak detector, where the peakvalue for that row represents the confidence or “degree of fit” of thematched filter to the reflection data. The larger the magnitude of thepeak value the better the match and the more confidence that the filtermodel and adaptation accurately characterize the reflection data. Thecomputed output of the Correlator is a vector of confidence factors 77a, where the vector contains one confidence factor for each depth in theenhanced time-domain reflection matrix.

[0074] After cross-correlation, the DC Filter is applied in block 132and the Peak Detector is applied in block 134 of FIG. 11. The peak valuedetermined becomes the confidence metric for the estimate (as found bythe Estimator). The peak values, one at each depth for eachphysiological process, are sent to the Selector 78.

[0075] The Correlator may perform more than one correlation perinvocation. For example, it may correlate a model for cardiac rate andone for pulmonary rate, producing a unique confidence metric for eachprocess at each depth.

[0076] Selector

[0077] The last stage in the Extractor 44 of FIG. 4 is the Selector 78of FIG. 6. For each physiological process under investigation, theselector takes the estimates from the Estimator 73 and the set ofconfidence measures 77 a from the AC Peak Detector 77, seen in FIG. 6,as input and produces a single “best fit” measure with a correspondingconfidence metric. A best fit measure and confidence metric pair are theoutput for each invocation of the above Selector algorithm operating ona single time-domain reflection matrix. This generates a pair of timeordered sequences as illustrated in the example below. The M's in themeasurement sequence represent the time-ordered measurements of thephysiological process being measured. The Q's represent thecorresponding confidence of each measurement.

[0078] If more than one physiological process is under investigation,the Selector may choose more than one value per invocation; depending onhow many value-confidence matrices it is given from the Correlator step.For example, it may select a value for cardiac rate and one forpulmonary rate.

[0079] Selector Output for Process M: Measurement M⁻³ M⁻² M⁻¹ M₀ M₁ M₂M₃ M₄ . . . M_(n) M_(n+1) . . . Sequence: Confidence Q⁻³ Q⁻² Q⁻¹ Q₀ Q₁Q₂ Q₃ Q₄ . . . Q_(n) Q_(n+1) . . . Metric Sequence:

[0080] On each invocation of the Selector, the simplest method forselecting the “best fit” measurement is to choose the one with thegreatest confidence metric. However, this scheme may be enhanced byconsidering confidence metrics at the depths adjacent to the depth withthe largest confidence magnitude. Depths with high confidence metricsbut low adjacent confidence metrics may be discarded in favor of a depthcentered in an area with generally-high confidence metrics.

[0081] A flow chart for the steps involved in the Selector is shown inFIG. 12. The physiological measurements are received from the Estimator73 in block 141 and the corresponding confidence metrics are receivedfrom the Correlator 77 in block 140. Given these two 1×N vectors, thedepth with the optimal measurement is selected in block 143 based on theselection criteria in block 144. Decision block 145 determines if theresults of the algorithm should be forwarded to the Focuser 48 so thatit can modify the system's operation. If this is the case, theinformation is sent to the Focuser via block 146. In any case, theselected physiological measurement and its corresponding confidencemetric are sent to the Analyzer 46 in block 147. These steps are run oneach invocation of the Selector for each physiological process beingmeasured.

[0082] Analyzer

[0083] The Analyzer 46 of FIG. 4 processes the time-ordered sequence ofvalues and confidence measures from Selector 78 of FIG. 6 and seeks forproblematic trends in the values. It can detect trends because, undernormal circumstances, a time-ordered sequence of measurements of aphysiological process should remain within a specific range and anyvariations should correspond to one of many well-known patterns.Excursions beyond appropriate ranges or deviations from expectedpatterns may signal a problematic trend. For example, while resting, aperson's cardiac rate should be low and exhibit little variation. As thelevel of activity increases, their cardiac rate will increase gradually.On the other hand, an exceedingly high heart rate or a rapid increase inheart rate may indicate that the person being monitored is experiencinga cardiac event.

[0084] A second technique is based on time series analysis where thenext incremental value in the series is predicted from one or more pastvalues and then, when the actual value arrives from the Selector, thedifference between it and the prediction is calculated. This differenceis the first order prediction error and suitable error thresholds can beapplied to the error terms. There are many techniques used in practiceto calculate the prediction term with one of the most common based onthe application of a “moving average filter of degree N” where the Nmost recent measurements are averaged and this average becomes theprediction of the next measurement. This process is illustrated below.

[0085] Original Measurement Sequence to time (n): M⁻³, M⁻², M⁻¹, M₀, M₁,M₂, M₃, M₄, . . . , M_(n),

[0086] Corresponding minimum and maximum thresholds on Measurement M:(M_(L),M_(H))

[0087] Generation of (n+1) prediction: M*_(n+1)=f(M); where f is afunction operating on one or more past values of M

[0088] Calculation of (n+1) error term: E_(n+1)=M_(n+1)−M*_(n+1)

[0089] Corresponding minimum and maximum thresholds on Error E:(E_(L),E_(H))

[0090] Alarm if “Mx(M_(L),M_(H))” OR “Ex(E_(L),E_(H))”

[0091] Additionally, time series analysis can be extended to calculationof higher order error terms where one or more past error terms are usedto create a prediction of future error terms. Calculation of higherorder error terms provides more detail on variations in the processunder investigation and may allow earlier detection of adverse events.For example, the second order error term can be derived by applying a“moving average filter of degree P” to the series of first order errorterms to obtain a prediction of the next first order error term. Thedifference between the prediction and the actual value is the secondorder error term.

[0092] A third analysis mechanism to detect a problematic trend is tomatch the sequence of values with a known problematic pattern throughcorrelation. This is accomplished by cross-correlating the time-orderedsequence with a known pattern. For example, for a given sequence ofcardiac rate measurements, the sequence could be cross-correlatedagainst patterns for bradycardia, tachycardia, and fibrillation todetermine if one of these conditions is occurring or developing.

[0093] The steps involved in the Analyzer are shown in flow chart inFIG. 13. The optimal value-confidence pair for the current invocation ofthe algorithm is received from the Selector 78 in block 150. TheAnalyzer may have several mechanisms to determine if an abnormal trendis developing. In block 151, each mechanism is used to check the streamof data. Decision block 152 determines if the current mechanism hasdetected a problematic trend. If it has, an alert is sent to thesystem's display in block 153. In any case, decision block 154determines if there is another mechanism to try. Mechanisms willcontinue to be tried even after an alert so that performance statisticscan be collected on each analysis mechanism. When all of the analysismechanisms have been performed, block 155 determines if feedback shouldbe sent to the Focuser 48. If so, it is sent in block 156. In any case,the value-confidence pair is stored in block 157 so that it can be usedin the future for detecting trends. These steps are run for eachphysiological process being measured.

[0094] If the Selector explained with respect to FIG. 12 is producingmultiple value-confidence pairs because more than one physiologicalprocess is being analyzed simultaneously, the Analyzer may process eachstream independently and may also use the values from each stream toassist in the analysis of the other stream. For example, in a systemdesigned to measure cardiac and pulmonary rates, the Analyzer wouldanalyze the cardiac and pulmonary streams independently looking fortrends, but could also compare the two streams because, for instance,increases in cardiac rate resulting from exertion should, normally,correspond to increases in pulmonary rate.

[0095] Focuser

[0096] The Focuser 48 of FIG. 4 is a control process that uses theresults from one or more of the device's stages to modify the amount andtypes of processing performed on each pass through the system. It takesinputs from the Extractor's Selector 78 of FIG. 6, and the Analyzer 46of FIG. 4 and, based on the values received and its internal decisionalgorithm, modifies the behavior of the Receiver 6 of FIG. 1 and theExtractor 44 of FIG. 4. The Focuser feedback mechanism is illustrated inFIG. 14.

[0097] The Focuser may increase, decrease or simply modify the amount ofcomputation done by the system on each iteration. For example, if thetrend being analyzed by the Analyzer is extremely stable, the Focusermay decrease the amount of computation to conserve power or resources.If, on the other hand, a problematic trend appears to be developing, itmay increase the level of computation.

[0098] One scheme to do this is to change the number of depths at whichdata is processed. That is, the reflections from some depths may beignored. For example, for a system designed to measure cardiac rate,only the depths near the heart may be processed if the trend is stableand measurements strong. This concept is illustrated in the time domainreflection matrix of FIG. 15. The matrix in FIG. 15 can hold reflectionvalues from m different depths in the body. However, the Focuser hasdetermined that only three depths are worthy of computation. They aretermed the “Depth Range of Interest” (shaded area 170) and only the datain those depths will be processed. The remaining data (shaded areas 171,172) will be ignored.

[0099] This depth-focusing process is accomplished by having theExtractor 46 take a Depth Range of Interest (DROI) indicator It willthen only process those entries in the reflections matrix that are inthe DROI. The DROI indicator is a 1-dimensional matrix of Boolean valueswhere entry i indicates whether not the Extractor should process datafrom depth i. The DROI indicator for the sample time domain reflectionmatrix in FIG. 15 is illustrated in FIG. 16. The depths that are in theDROI have a “true” value in their cell of the DROI. All other depthshave a “false” value.

[0100] An alternative focusing scheme is to vary the amount ofprocessing that is done for each depth on each pass through the system.For example, the frequency data may be correlated with only one model toconserve resources or with many models when the system needs to huntbroadly for the best-fit model.

[0101] Preferred Embodiment Example: An Algorithm for Measuring CardiacRate

[0102] The user interface for the cardiac rate algorithm is shown inFIG. 17. It illustrates the signals displayed as a result of variousintermediate and final calculations associated with the algorithm.

[0103] The term “bin” is used extensively in the user interface. It is acommon term from the study of radar and is synonymous with depth usedpreviously.

[0104] Bin Intensity Graph—The two-dimensional graph displays thetime-domain reflections acquired at varying depths from the chestsurface to the back in a human body. Depth is represented on thevertical axis, number of samples on the horizontal axis, and amplitude(or reflection strength) mapped as a color intensity ranging from blackto white across a blue spectrum (white is the greatest signal strength).This graph shows 20,000 samples per depth, which translates to 10seconds of data at each depth. For operation in range gate mode, thesamples must be collected over a time period that is greater than theslowest expected physiological cycle period.

[0105] Bin Power Spectrum—After the time-domain data has been processedthrough a Fast Fourier Transform, the frequency domain results are shownin this two-dimensional graph. Depth is represented on the verticalaxis, frequency on the horizontal axis, and amplitude mapped as a colorintensity ranging from black to white across a blue spectrum. In thisgraph, amplitude refers to the strength of the corresponding frequencycomponent in the time-domain data. The discrete vertical lines arethought to be due to 60 cycle noise and system clocks.

[0106] BPM, Amplitude vs. Bin—The two-dimensional graph displays boththe frequency (green trace) and amplitude (magenta blocks) of the“primary tone” per depth (or bin). The primary tone is the frequencywith the greatest amplitude coefficient in each bin. The depth isrepresented on the horizontal axis with the frequency and amplitude onthe vertical axis. In addition, the light brown vertical dotted linelabeled “Detected” shows the bin that algorithm has selected as thecardiac location. The selected bin is the one with the highestcorrelation between the frequency data and the cardiac waveform model.The blue dashed upper and lower limits show the range in which cardiacdata is valid (i.e., 40 to 300 beats per minute).

[0107] Frequencies outside those bounds are ignored.

[0108] Source Statistics—This panel displays the current data file namealong with its attributes: number of bins, number of samples per bin,and sample rate in hertz.

[0109] Results—This panel displays the final cardiac rate in beats perminute as determined by the algorithm.

[0110] Distribution—The two dimensional graph displays the distributionof primary tones for cardiac data for the various depths. For example,the graph shows that there were approximately 19 depths that had primarytones between 0 and 40, and approximately 10 depths that had primarytones between 40 and 105.

[0111] While the foregoing has been with reference to particularembodiments of the invention, it will be appreciated by those skilled inthe art that changes in these embodiments may be made without departingfrom the principles and spirit of the invention, the scope of which isdefined by the appended claims.

We claim:
 1. In a device employing ultra wideband radar return signalsfor extracting physiological data from one or more bodily organs orphysiological processes of a patient, said device having anelectronically controlled range gate, the process of extracting accuratephysiological data including the steps of employing frequency spectrumanalysis and statistical filtering techniques in extracting said data.2. The process of claim 1 further including the steps of searching for,acquiring, tracking, and interrogating one or more of said bodily organsor physiological processes by electronically controlling range gatesettings of said device to depths of interest within the patient anddynamically adjusting those settings to optimize desired ones of saidphysiological data.
 3. The process of claim 2 including the range gatemode step of holding the range gate at one or more of said depths whileextracting said physiological data.
 4. The process of claim 2 includingthe range finder mode step of sweeping the range gate across a range ofsaid depths while extracting said physiological data.
 5. The process ofclaim 2 including the range finder mode step of electronically steppingthe range gate through a series of said depths while extracting saidphysiological data.
 6. The process of claims 3, 4 or 5 including thesteps of selecting the method of operation of the range gate andcontrolling said gate by one or more control algorithms employing as areference a statistical model of one or more of said bodily organs orphysiological processes, and comparing said reference to incoming radarreturn signals to determine the optimal range gate selection for desiredphysiological data extraction.
 7. In a device employing ultra widebandradar return signals for extracting physiological data from one or morebodily organs or physiological processes of a patient, the process ofelectronically fixing a sample depth within the body from whichphysiological data are to be extracted and collecting a number ofsamples at said sample depth, said process including the steps ofemploying frequency spectrum analysis and statistical filteringtechniques in extracting said data.
 8. The process of claim 7 includingthe further steps of, after said collecting, electronically changingsaid sample depth and collecting a number of samples at said changedsample depth.
 9. In a device employing ultra wideband radar returnsignals for extracting physiological data from one or more bodily organsor physiological processes of a patient, the process of electronicallyselecting a plurality of sample depths within the body corresponding tothe area for which physiological data are to be extracted and collectingdata samples at a plurality of said plurality of sample depths.
 10. Theprocess of claim 9 including the steps of collecting said data samplesat discrete ones of said plurality of sample depths.
 11. The process ofclaim 9 including the steps of collecting said data samples atcontinuously varying ones of said plurality of sample depths.
 12. Adevice employing ultra wideband radar return signals for extractingphysiological data from a patient, the device including a pulsegenerator and a range delay circuit coupled to said generator forreceiving a pulse train therefrom, said range delay circuit including adigital to analog converter coupled to (a) at least one operationalamplifier and (b) at least one single-shot multivibrator, said at leastone single-shot multivibrator receiving at least (i) a trigger signalfor causing the single-shot multivibrator to initiate a single pulsecycle for substantially every pulse in the pulse train and (ii) ahold-off signal to determine the period of the pulses in the pulsecycle.
 13. In a device employing ultra wideband radar return signals forextracting physiological data from a patient, the combination of (a) apulse repetition frequency generator for providing a pulse train coupledto a transmitter and a receiver, said pulse repetition generatorincluding a voltage controlled oscillator having at least one controlport as the source of the pulse train, and (b) a source of dithersignals coupled to the output of the pulse repetition frequencygenerator to inject randomized noise signal into said at least onecontrol port to cause spectral spreading.
 14. The device of claim 13wherein said dither signals provide a unique transmit coding pattern forsaid device.
 15. A device employing ultra wideband radar return signalsfor extracting physiological data from one or more organs orphysiological processes of a patient at one or more depths of interestwithin said patient, the device including a pulse repetition frequencygenerator for providing a pulse train coupled to a transmitter and areceiver, each of said transmitter and receiver coupled to one or moreantennas, said transmitter transmitting pulse signals to said patientand said receiver receiving return signals from said patient, saidreceiver connected to an analog to digital converter and furtherincluding dual channels and a sample and hold circuit coupled to eachchannel, each said sample and hold circuits triggered by the pulserepetition frequency generator, said circuits coupled to integratorelements and including a range compensated gain circuit includingblanking to eliminate undesired ones of said return signals.
 16. Thedevice of claim 15 further including feed-forward circuitry including anamplifier coupled to a low-pass filter producing a filtered signal forremoving from the output of said amplifier frequency components relatedto movement of said one or more organs, said low-pass filter coupled asan input to a subtractor circuit having as one input the output of saidamplifier, said subtractor element subtracting said filtered signal fromsaid output.
 17. The device of claim 15 including anti-aliasingcircuitry and amplification circuitry prior to said analog to digitalconverter.
 18. The device of claim 17 wherein said anti-aliasingcircuitry includes circuitry including circuit elements exhibitingelectrical characteristics of a Butterworth filter, a Bessel filter, aChebychev filter or an elliptic filter.
 19. The device of claim 15wherein said analog to digital converter is coupled to a signalprocessor component and operates on the analog signal from said receiverto provide a digitized signal, said digitized signal being processed bysaid signal processor component to extract pertinent physiological data.20. The device of claim 19 wherein said signal processor componentconverts said digitized signal to a format which is (a) tracings ofamplitude versus time for at least some of said one or more depths ofinterest; (b) power spectral density for at least some of said one ormore depths of interest; (c) time domain histograms for at least some ofsaid one or more depths of interest; (d) frequency domain histograms forat least some of said one or more depths of interest; (e) a numericalvalue for at least one physiological function; or (f) confidence factorsfor one or more of the said formats.
 21. The device of claim 20 whereinsaid converted digitized signal is displayed on a display device.
 22. Ina device employing ultra wideband radar return signals for extractingphysiological data from one or more organs or physiological processes ofa patient at one or more depths of interest within said patient, saiddevice capable of initiating radar sweep cycles, said device includinga. a pulse repetition frequency generator coupled to a transmitter andreceiver for providing a pulse train thereto, each of said transmitterand receiver coupled to one or more antennas, said transmittertransmitting pulse signals to said patient, and b. a receiver receivingreturn signals in analog form from said patient, said device including asignal processor component and an analog to digital converter forconverting said return signals to digital form for processing saidreturn signals for presentation to a user, the processing including thesteps of i. preprocessing and digitizing said analog return signals toprovide enhanced, digitized return signals, and ii. extracting from saidditigized signals one or more signals representing desired physiologicaldata including a sequence of values and confidence measures.
 23. Theprocess of claim 22 further including the step of said receiveroperating on said analog return signals by suppressing certainhigh-strength return signals and amplifying signals representing motionwithin said patient.
 24. The process of claim 23 wherein thepreprocessing step includes low-pass filtering said suppressed andamplified analog return signals, said low pass filtering passingfrequencies up to 5 Hz, and minimizing the potential of aliasing priorto said digitizing step.
 25. The process of claim 23 wherein thepreprocessing step includes low-pass filtering said suppressed andamplified analog return signals, said low pass filtering passingfrequencies up to 200 Hz, and minimizing the potential of aliasing priorto said digitizing step.
 26. The process of claim 25 wherein saidlow-pass filtering is accomplished by passing said suppressed andamplified return signals through a filter circuit including circuitelements exhibiting electrical characteristics of a Butterworth filter,a Bessel filter, a Chebychev filter and an elliptic filter.
 27. Theprocess of claim 24 wherein the step of low pass filtering includes thesteps of filtering said return signals to produce filtered signals andsampling said filtered signals, said filter having filter parameters andsaid sampling being at a sampling rate such that the amplitude of saidreturn signals at frequencies equal to or greater than half the samplerate are less than the minimum amplitude detectable by said analog todigital converter.
 28. The process of claim 25 wherein said step oflow-pass filtering includes the steps of filtering said return signalsto produce filtered signals, and sampling said filtered signals, saidfilter having filter parameters and said sampling being at a samplingrate such that the amplitude of said return signals at frequencies equalto or greater than half the sample rate is less than the minimumamplitude detectable by said analog to digital converter.
 29. Theprocess of claim 24 wherein the digitizing step includes the step ofconverting a return signal from analog to digital in saidanalog-to-digital converter, said converter accepting analog inputsignals and producing digital output signals, to translate said returnsignals into a series of discrete numerical values representing theamplitudes of said return signals to provide an input signal for theextracting step.
 30. The process of claim 29 wherein saidanalog-to-digital converter is synchronized with (a) changes in depth inboth said range gate mode and said range finder mode and (b) initiationof a new one of said sweep cycles in the case of said range finder mode.31. The process of claim 30 wherein some or all of the output of saidanalog-to-digital converter is stored in storage in a two-dimensionalmatrix of time-sampled return signal values.
 32. The process of claim 31including the step of reducing data from said output prior to saidoutput being stored in said matrix.
 33. The process of claim 31including the step of reducing data stored in said two-dimensionalmatrix after said data is stored in said matrix.
 34. The process ofclaim 31 wherein the two-dimensional matrix stores (a) ones of saidoutput signals representing return signals collected at a particulartime sample interval with respect to the synchronization signal and (b)ones of said output signals representing return signals collected from adepth within the patient.
 35. The process of claim 31 wherein the depthof said one or more depths within the patient from which said returnsignals are collected is fixed and a number of samples of said returnsignals are collected at said fixed depth over a period of time.
 36. Theprocess of claim 35 wherein said two-dimensional matrix is filled by rowordering.
 37. The process of claim 31 wherein the depth of said one ormore depths within the patient from which return signals are collectedis varied over a finite range of said depths of interest and samples ofsaid return signals are collected at a plurality of said depths.
 38. Theprocess of claim 37 including the step of collecting said data samplesat one of said one or more depths of interest.
 39. The process of claim37 including the step of collecting said data samples at continuouslyvarying ones of said one or more depths of interest.
 40. The process ofclaim 37 wherein said two-dimensional matrix is filled by columnordering.
 41. The process of claim 29 wherein said converting stepincludes the step of collecting sub-samples of said return signals fromsaid samples to reduce the volume of time-sampled data.
 42. The processof claim 41 including the step of collecting said sub-samples andsubsequently loading them into a two-dimensional matrix in computerreadable storage.
 43. The process of claim 41 including the step ofloading said samples into a two-dimensional matrix in storage andsubsequently collecting said sub-samples from said samples.
 44. Theprocess of claim 29 wherein the converting step includes the step ofcoarse quantizing to increase contrast and reduce computationalcomplexity of said return signals.
 45. The process of claim 29 whereinthe converting step includes the step of normalizing to maximize thedynamic range of said return signals.
 46. The process of claim 22wherein said extracting step includes the step of converting thedigitized return signals into signals representing useful physiologicaldata.
 47. The process of claim 46 wherein said useful physiological datarepresent cardiopulmonary data.
 48. The process of claim 47 wherein saiduseful cardiopulmonary data includes one or more information signalsselected from the group of information signals representing rate,rhythm, cardiac chamber volume, cardiac stroke volume, cardiac ejectionfraction, and cardiac output.
 49. The process of claim 22 wherein theextracting step includes operating on files of said return signalscollected at a time earlier than the time of said step of operating. 50.The process of claim 22 wherein the extracting step includes operatingon continuous ones of said return signals in real time.
 51. The processof claim 22 wherein the extracting step includes collecting returnsignals from a particular one of said one or more depths of interestwithin a patient.
 52. The process of claim 22 wherein the extractingstep includes the step of transforming time sampled return signals tothe frequency domain for spectral processing.
 53. The process of claim52 wherein said transforming step includes employing a Fast FourierTransform.
 54. The process of claim 52 wherein said transforming stepincludes employing a discrete cosine transform.
 55. The process of claim52 wherein the transforming step includes employing a discrete filterbank.
 56. The process of claim 54 or 55 wherein the result of saidtransforming step is a two dimensional frequency domain reflectionsignal matrix stored in computer readable storage, one dimension of saidmatrix including cells containing coefficients of frequency signals thereal value of which corresponds to the amplitude of the frequencycoefficient of one of said return signals at one of said one or more ofsaid depths of interest within said patient.
 57. The process of claim 53wherein the result of said transforming step is a two dimensionalfrequency domain reflection signal matrix in computer readable storage,one dimension of said matrix including cells containing coefficients offrequency signals the complex value of which corresponds to the real andimaginary components of the frequency coefficient of one of said returnsignals at one or more of said depths of interest within said patient.58. The process of claim 53, 54 or 55 wherein said transforming iscontrolled by a focuser to prohibit transforming ones of said returnsignals received from predetermined depths within said patient.
 59. Theprocess of claim 22 wherein the extracting step includes operating on afrequency domain reflection signal matrix and estimating an approximatevalue for said physiological data.
 60. The process of claim 59 whereinsaid estimating an approximate value includes obtaining maximum datavalue in each row of said frequency domain reflection signal matrix bydetermining the highest amplitude frequency components of return signalsfor a plurality of depths of said one or more depths of interest withinsaid patient.
 61. The process of claim 60 wherein said data peaks are inthe form of a two-dimensional matrix of signal entries stored incomputer readable storage, said matrix containing a pair of values forreturn signals from at least some of said one or more depths of interestwithin said patient.
 62. The process of claim 61 including the furtherstep of filtering the signal entries in said two-dimensional matrix toremove those entries where the frequency value lies outside an expectedrange of frequencies.
 63. The process of claim 62 wherein said filteringstep includes applying a bandpass filter with a pass band from 0.5 Hz to5 Hz to the frequency values in said two-dimensional matrix forapplications related to cardiac functions.
 64. The process of claim 61including the steps of filtering the signal entries in saidtwo-dimensional matrix by applying multiple filters with differentfiltering characteristics to said signal entries to estimate more thanone type of physiological data.
 65. The process of claim 22 wherein saidextracting step includes the step of employing a modeler component toadapt one or more models from a data base containing one or more filtermodels representing a single cycle of physiological data, into a matchedfilter for subsequent cross-correlation with certain of said returnsignals.
 66. The process of claim 65 wherein said return signals forcorrelation are time-based return signals.
 67. The process of claim 65wherein said return signals for correlation are frequency-based returnsignals.
 68. The process of claim 65 wherein one of said one or moremodels is obtained by self-convolving a single cycle input pattern thatmodels an expected extracted physiological data signal to produce amatched filter for said physiological data signal.
 69. The process ofclaim 68 wherein said input patterns are sinusoids, patterns developedthrough theoretical studies of expected return signals, or actualpatterns from individual patients.
 70. The process of claim 69 whereinsaid input patterns include patterns developed from signals representingnormal physiological data and patterns developed from signalsrepresenting abnormal physiological data.
 71. The process of claim 70wherein said abnormal physiological data include data corresponding tobradycardia, tachycardia and fibrillation.
 72. The process of claim 22wherein said extracting step includes using matched filters andcorrelation to calculate an array of numerical rate values andassociated confidence factors for said physiological data.
 73. Theprocess of claim 65 including the additional step of cross correlatingone of said one or more filter models from said modeler component withan original return signal matrix to produce a series of correlationcoefficient vectors with one vector for each of said one or more depthsper cross-correlated filter model.
 74. The process of claim 73 whereinsaid return signal matrix is a time-based return signal matrix.
 75. Theprocess of claim 73 wherein said return signal matrix is afrequency-based return signal matrix.
 76. The process of claim 73wherein each of said vectors is DC-filtered to remove common bias. 77.The process of claim 76 wherein each said DC-filtered vector is operatedon by a peak detector component, the maximum data value obtained by saidcircuit for each said vector representing the degree of fit of the crosscorrelated filter model to the time-based physiological data, saiddegree of fit representing the accuracy of the frequency estimate to theactual frequency of said return signals, the output of said peakdetector component being a vector representing a set of confidencemetrics, the members of said set being the confidence factors for eachreturn signal from said one or more depths within said patient at whichsaid return signals are collected.
 78. The process of claim 73 whereinsaid additional step is repeated for a plurality of said one or moremodels.
 79. The process of claims 59 and 72 including the additionalstep of operating on said sequence of values and said confidence factorsto produce a single best fit measure and corresponding confidence factorfor each of said return signals from said one or more organs orphysiological processes.
 80. The process of claim 79 wherein said stepof producing a single best fit measure includes (i) choosing theapproximate value with the single largest amplitude confidence factor,or (ii) choosing return signals from one of said one or more depthswithin said patient, said one depth generally providing return signalswith generally high amplitude confidence factors.
 81. The process ofclaim 72 further including an analyzing step including the step ofprocessing said time-ordered sequences of said numerical rate values andassociated confidence factors to determine problem trends in saidnumeral rate values.
 82. The process of claim 81 wherein said analyzingstep includes (i) detecting excursions in said rate values beyondappropriate ranges, (ii) detecting deviations of said rate values fromexpected patterns, (iii) performing a time series analysis of saidsequence of said rate values in which the next incremental value in saidsequence is predicted from one or more past values thereof andcalculating the difference between said next incremental value and saidpredicted value and using said difference as the first order error term,and detecting excursions in said error terms beyond appropriate ranges,(iv) performing a time series analysis based on higher-order error termsby extending step (iii) to the derivation of second, third, fourth, andhigher error terms, or (v) matching said sequence of rate values with aknown problem pattern by cross correlating said sequence against a knownproblem pattern.
 83. The process of claim 82 wherein said sequence ofrate values is cross-correlated against a pattern for bradycardia. 84.The process of claim 82 wherein said sequence of rate values iscross-correlated against a pattern for tachycardia.
 85. The process ofclaim 82 wherein said sequence of rate values is cross-correlatedagainst a pattern for fibrillation.
 86. The process of claim 79 furtherincluding a step of determining whether multiple groups of pairs of saidvalues and confidence factors are being selected because more than oneset of physiological data are being analyzed simultaneously and, as aresult of said determining step, processing each of said pairs of valuesand confidence factors independently and using the values in one of saidgroup of said pairs to analyze another of said group of said pairs. 87.The process of claim 22 wherein including the further step of applying acontrol process that includes changing the depths of said one or moredepths at which return signals are collected by using a depth range ofinterest indicator within a matrix of said return signals and processingonly those entries in said matrix within the depth range of interest.88. The process of claim 22 including the further step of applying acontrol process that includes the step of varying the amount ofprocessing performed on said return signals from each of said one ormore depths on each operation cycle of said device by (a) correlating,for subsequent cross-correlation with certain of said return signals,only one of a plurality of models from a data base of said models toconserve computation resources, or (b) correlating many of saidplurality of models to seek broadly for a best-fit model.
 89. Electronicradar return signals embodied in processor readable storage, saidsignals representing a physiological process and presented as atwo-dimensional graph of time-domain return signals acquired by a deviceemploying ultra wideband radar return signals for extractingphysiological data from one or more organs or physiological processes ofa patient at one or more depths of interest within said patient, saiddevice capable of performing the process steps of using statisticalmodels of expected return signals from said one or more areas forcollecting signal samples at various of said depths.
 90. The electronicradar return signals of claim 89 wherein a first of said dimensionsrepresents said depths on a vertical axis, and a second of saiddimensions represents a number of samples taken at said depths on ahorizontal axis, and strength of said return signals is represented as acolor intensity ranging across a color spectrum.
 91. The electronicradar return signals of claim 90 wherein said color spectrum ranges froma first color to one or more second colors to provide sufficient colordiscrimination, said color discrimination being hue, saturation orluminance.
 92. The electronic radar return signals of claim 90, saidsamples being collected over a time period that is greater than half ofthe shortest expected cycle period of said physiological process. 93.Electronic radar return signals embodied in processor readable storagerepresenting time-domain representations of radar return signalsacquired by a device employing ultra wideband radar return signals forextracting physiological data from one or more organs or physiologicalprocesses of a patient at one or more depths of interest within saidpatient by using statistical models of expected ones of said returnsignals from at least some of said one or more depths, said radar returnsignals in a time domain operated on by a Fast Fourier Transform totransform said signals into a frequency domain, said electronic signalsrepresented in two-dimensional graphical form wherein depth isrepresented on a vertical axis, frequency on a horizontal axis, andamplitude is mapped as a color intensity.
 94. The electronic radarreturn signals of claim 93 wherein said signals include a representationof color luminance and said color luminance ranges from black to whiteacross a spectrum of a color other than black and white.
 95. Theelectronic radar return signals of claim 94 wherein amplitude refers tothe strength of the corresponding frequency component in the time-domainsignal.
 96. Electronic radar return signals embodied in processorreadable storage, said signals representing a physiological process andpresented as a two-dimensional graph of time-domain return signalsacquired by a device employing ultra wideband radar return signals forextracting physiological data from one or more organs or physiologicalprocesses of a patient at one or more depths of interest within saidpatient by using statistical models of expected ones of said returnsignals from at least some of said one or more depths, wherein said twodimensional graph represents frequency in a first color trace andamplitude in a second color trace of the primary tone per depth for eachof a plurality of said one or more depths.
 97. The electronic radarreturn signals of claim 96 wherein said primary tone is the frequencywith the greatest amplitude at each said depths.
 98. The electronicradar return signals of claim 97 wherein depth is represented on ahorizontal axis and the frequency and amplitude on a vertical axis. 99.The electronic radar return signals of claim 98 further including asignal indicating the depth of said one or more depths having thehighest correlation between the frequency data of said return signalsand a statistical model of one or more of said bodily organs orphysiological processes.
 100. The electronic radar return signals ofclaim 99 wherein said statistical model is the model of a cardiacwaveform.
 101. The electronic radar return signals of claim 100 furtherincluding an electronic signal indicating the range in which saidphysiological data are valid.
 102. The electronic radar return signalsof claim 101 wherein said physiological data represent cardiac data andsaid range is 30 to 300 beats per minute.
 103. The electronic radarreturn signals of claim 102 further including an electronic signalindicating the current data file name along with its attributes. 104.The electronic radar return signals of claim 103 wherein said attributesinclude the number of discrete depths at which data was collected andstored in the frequency domain reflection matrix, the number of samplestaken at each of said discrete depths, and the sample rate in Hertz.105. The electronic radar return signals of claim 96 further includingan electronic signal indicating the distribution of primary tones forcardiac data for a plurality of said depths.
 106. A graphical userinterface embodied in processor readable storage, said graphical userinterface capable of being displayed on a display device and includingelectronic radar return signals embodied in processor readable storage,said return signals representing a physiological process and presentedas a two-dimensional graph of time-domain signals acquired by a deviceemploying ultra wideband radar return signals for extractingphysiological data from one or more organs or physiological processes ofa patient at one or more depths of interest within said patient, byusing statistical models of expected ones of said return signals incollecting signal samples at various ones of said one or more depths.107. A graphical user interface embodied in processor readable storage,said graphical user interface capable of being displayed on a monitor,and including electronic radar return signals embodied in processorreadable storage representing time-domain representations of radarreturn signals acquired by a device employing ultra wideband radarreturn signals for extracting physiological data from one or more organsor physiological processes of a patient at one or more depths ofinterest within said patient, by using statistical models of expectedones of said return signals in collecting signal samples at various onesof said one or more depths, said radar return signals in a time domainbeing operated on by a Fast Fourier Transform to transform said signalsinto a frequency domain, said electronic signals represented intwo-dimensional graphical form wherein depth is represented on avertical axis, frequency is represented on a horizontal axis, andamplitude is represented as a color intensity.
 108. A graphical userinterface embodied in processor readable storage, said graphical userinterface capable of being displayed on a display device, and includingelectronic radar return signals embodied in processor readable storage,said return signals representing a physiological process and presentedas a two-dimensional graph of time-domain return signals acquired by adevice employing ultra wideband radar return signals for extractingphysiological data from one or more organs or other physiologicalprocesses of a patient at one or more depths of interest within saidpatient, by using statistical models of expected ones of said returnsignals in collecting signal samples at various ones of said one or moredepths, wherein said two dimensional graph represents frequency in afirst color trace and amplitude in a second color trace of the primarytone per depth, for each of a plurality of said one or more depths. 109.In a device employing ultra wideband radar return signals for extractingphysiological data from one or more areas of a patient, the process ofextracting accurate physiological data including the step of usingstatistical models of expected return signals from said one or moreareas.
 110. The device of claim 10 further including a static reflectionsuppression device and an anti-aliasing filter.
 111. In a deviceemploying ultra wideband radar return signals for extractingphysiological data from one or more depths in a patient as reflectedsignals, by using statistical models of expected ones of said returnsignals in collecting signal samples at various ones of said one or moredepths, the process of operating on digitized reflected signals in atime domain reflection matrix by a reducer, said process including oneor more of the steps of (a) sub-sampling said signals to reduce thevolume of said signals, (b) coarsely quantizing said reduced volume toincrease contrast and reduce the computational complexity thereof, and(c) normalizing said reduced volume to maximize dynamic range to producea second two-dimensional matrix containing enhanced time-sampledreflected signals.
 112. The process of claim 111 including the furtherstep of converting said enhanced time-sampled reflected signals tosignals in the frequency domain for spectral processing.
 113. Theprocess of claim 112 wherein said converting step includes the steps ofemploying a Fast Fourier transformation algorithm.
 114. The process ofclaim 112 wherein said converting step includes the step of employing aDiscrete Cosine Transform.
 115. The process of claim 112 wherein saidconverting step includes the step of employing a Discrete Filter Bank.116. The process of claims 113, 114 or 115 wherein said signals in thefrequency domain are in a matrix wherein each cell of said matrixcorresponds to the frequency coefficient at a given one of said one ormore depths.
 117. The process of claim 116 wherein the converting stepincludes employing a Fast Fourier Transformer and the value of each cellis a complex coefficient having a real and imaginary components. 118.The process of claim 117 including the steps of operating on said realand imaginary components by employing trigonometric identities toconvert said components to signals representing magnitude and phase.119. A plurality of signals stored in two dimensional storage saidstored signals representing signals reflected from a depth of a patientby a device employing ultra wideband radar return signals for extractingphysiological data from one or more depths in a patient as reflectedsignals, by using statistical models of expected ones of said returnsignals in collecting signal samples at various ones of said one or moredepths, by using statistical models of expected ones of said returnsignals in collecting signal samples at various ones of said one or moredepths, said stored signals being in the frequency domain wherein thestored signals in one dimension of said storage represents at least oneof said one or more depths and the stored signals in another dimensionof said storage represents the frequency of said reflected signal atsaid at least one other of said one or more depths.
 120. In a deviceemploying ultra wideband radar return signals for extracting data fromone or more physiological processes being measured, from one or moredepths in a patient as reflected signals, by using statistical models ofexpected ones of said return signals in collecting signal samples atvarious ones of said one or more depths, the process of operating onsignals stored in two-dimensional storage in a frequency domain matrixby an estimator to derive a set of signals representing approximatevalues for said physiological data for a plurality of said one or moredepths.
 121. The process of claim 120 wherein said set of signals isoperated on by a modeler algorithm to adapt one or more matched filtersfrom a database including a plurality of matched filters, each of saidone or more matched filters developed by self-convolving one of aplurality of single cycle patterns of data representing extractedphysiological data to produce a matched filter for said one of saidplurality of single cycle patterns.
 122. The process of claim 121wherein the step of convolving is the step of generating a matchedfilter MF(n) from a discrete one of said plurality of patterns of lengthN, said pattern designated by P(n), by the discrete convolutionalgorithm:${{MF}(n)} = {\sum\limits_{m = 0}^{m = N}{{P(m)}{{P\left( {n - m} \right)}.}}}$


123. The process of claims 121 and 122 wherein said database includesfilters representing normal ones of said patterns and abnormal ones ofsaid plurality of patterns resulting from a plurality of ailments. 124.The process of claim 121 including the steps of: (a) receiving from saidestimator the estimates for the i-th physiological process beingmeasured where i ranges from 1 to said number of physiological processesbeing measured; (b) selecting matched filters from said database; (c)receiving feedback information indicating which of said filters of saiddatabase have been providing the best results; (d) analyzing saidfeedback information and, based on said analyzing step, eliminating oneor more of said matched filters in said database from consideration toselect a set of matched filters; and (e) customizing said set of matchedfilters.
 125. The process of claim 124 including the further step offorwarding said set for correlation.
 126. The process of claim 124including the further steps of repeating steps (a) through (e) for eachphysiological process being measured.
 127. The process of claim 124wherein said customizing includes the steps of (a) operating on said setof filters generate a first-order estimate of a cardiac rate for eachdepth in the vector and (b) adapting each of said filters of saiddatabase to the estimated rate through expansion or contraction of theperiod of each of said plurality of single cycle patterns.
 128. Theprocess of claim 127 wherein said one of said matched filters is basedon a half cycle sinusoid with a nominal period of 1 second.
 129. Theprocess of claim 121 including the steps of calculating a matrix ofcorrelation coefficients for measurements of a physiological process ofsaid one or more physiological processes by cross-correlating a set offilter models with a matrix of signals representing return signals toproduce a series of correlation coefficient matrices with one matrix foreach matched filter model set.
 130. The process of claim 129 whereinsaid matrix of signals is a time-based matrix.
 131. The process of claim129 wherein said matrix of signals is a frequency-based matrix.
 132. Theprocess of claim 129 wherein Di is the i-th depth of said one or moredepths, said cross-correlation of a single one of said matched filtersMF(n) of length N and the return signal from depth Di of length M isgiven by the following formula:${{R_{{MF},D_{i}}(p)} = {\sum\limits_{m = 0}^{m = M}{{{MF}(m)}D_{i}\left( {p + m} \right)}}};\text{for}$p  being  a  member  of  the  set  (−N, M)


133. The process of claim 132 including the steps of DC filtering eachrow of each matrix in said series of correlation coefficient matrices toremove common bias; and passing signals representing said filtered rowsthrough a peak detector, the output of said peak detector being a matrixof signals wherein the peak value of said signal in each row of saidmatrix represents the degree of fit of said matched filter to saidreturn signal.
 134. The process of claim 53 wherein the result of saidtransforming step is a two dimensional frequency domain reflectionsignal matrix in computer readable storage, one dimension of said matrixincluding cells containing coefficients of frequency signals the complexvalue of which corresponds to the amplitude and phase of the frequencycoefficient of one of said return signals at one or more of said depthsof interest within said patient.
 135. The process of claim 61 whereinsaid pair of values comprises (1) the frequency of the signal having themaximum frequency coefficient and (2) the value of said frequencycoefficient.
 136. The electronic radar return signals of claim 89wherein said process steps collect said signal samples from variousdepths of interest within said patient from front to back in thepatient's body.
 137. The electronic radar return signals of claim 89wherein said process steps collect said signal samples from variousdepths of interest within said patient from back to front in thepatient's body.
 138. The electronic radar return signals of claim 89wherein said process steps collect said signal samples from variousdepths of interest within said patient by randomly varying said depthsacross a predetermined range of depths.
 139. In a device employing ultrawideband radar return signals for extracting physiological data from oneor more depths in a patient as reflected signals, by using statisticalmodels of expected ones of said return signals in collecting signalsamples at various ones of said one or more depths, the process ofoperating on signals stored in two-dimensional storage in a frequencydomain matrix by an estimator to derive a set of signals representingapproximate values for said physiological data for a plurality of saidone or more depths.
 140. The process of claim 22 wherein said sequenceof values is time-ordered.
 141. In a device employing ultra widebandradar return signals for extracting physiological data from one or moreorgans or physiological processes of a patient at one or more depths ofinterest within said patient, said device capable of initiating radarsweep cycles, said device including a. a pulse repetition frequencygenerator coupled to a transmitter and receiver for providing a pulsetrain thereto, each of said transmitter and receiver coupled to one ormore antennas, said transmitter transmitting pulse signals to saidpatient, and b. a receiver receiving return signals in analog form fromsaid patient, said device including a signal processor component and ananalog to digital converter for converting said return signals todigital form for processing said return signals for presentation to auser, the processing including the steps of i. preprocessing anddigitizing said analog return signals to provide enhanced, digitizedreturn signals, ii. extracting from said digitized signals one or moresignals representing desired physiological data including a sequence ofvalues and confidence measures, iii. analyzing said values andconfidence measures to detect signals representing problematic medicaltrends in said values, and iv. applying a control process to one or moreof the steps of preprocessing, digitizing and extracting to modify theamount and types of processing performed within the device.
 142. Theprocess of claim 141 wherein said extracting step includes using matchedfilters and correlation to calculate an array of numerical rate valuesand associated confidence factors for said physiological data and saidanalyzing step includes the step of processing said time-orderedsequences of said numerical rate values and associated confidencefactors to determine problem trends in said numeral rate values. 143.The process of claim 142 wherein said analyzing step includes (i)detecting excursions in said rate values beyond appropriate ranges, (ii)detecting deviations of said rate values from expected patterns, (iii)performing a time series analysis of said sequence of said rate valuesin which the next incremental value in said sequences is predicted fromone or more past values thereof and calculating the difference betweensaid next incremental value and said predicted value and using saiddifference as the first order error term and detecting excursions insaid error terms beyond appropriate ranges, (iv) performing a timeseries analysis based on higher-order error terms by extending step(iii) to the derivation of second, third, fourth, and higher errorterms, or (v) matching said sequence of rate values with a known problempattern by cross correlating said sequence against a known problempattern.
 144. The process of claim 141 wherein the step of applying acontrol process includes the step of varying the amount of processingperformed on said return signals from each of said one or more depths oneach operation cycle of said device by (a) correlating, for subsequentcross-correlation with certain of said return signals, only one of aplurality of models from a data base of said models to conservecomputation resources, or (b) correlating many of said plurality modelsto seek broadly for a best-fit model.
 145. The process of claim 22wherein said extracting step includes the steps of employing a modelercomponent to adapt a plurality of models from a data base containing aplurality of filter models representing physiological data into aplurality of matched filters and cross-correlating said plurality ofmatched filters from said modeler component with a matrix of said returnsignals to produce a series of correlation coefficient vectors with onevector for each of said one or more depths per cross-correlated filtermodel.
 146. The process of claim 22 wherein said extracting stepincludes the step of storing a plurality of digitized ones of saidreturn signals from said analog to digital converter in a time domainreflection matrix comprising a plurality of rows of storage, andsubtracting each of a plurality of said digitized ones of said returnsignals from a representative signal of said plurality of digitized onesof said return signals to obtain specific optimized data signals. 147.The process of claim 146 wherein said representative signal is stored inthe first row of said time domain reflection matrix.
 148. The process ofclaim 146 wherein said representative signal is the average value ofeach amplitude from each of said rows of said time domain reflectionmatrix.
 149. The process of claim 146 wherein said plurality of rowscomprises at least eight rows and said representative signal is theaverage of each amplitude from each of the first eight rows of saidmatrix.
 150. The process of claim 146 wherein said plurality of rowscomprises at least eight rows and said representative signal is theaverage of each amplitude from each of the immediately preceding eightrows of said matrix.
 151. The device of claim 15 further including afeedback filter producing a filtered signal for removing from the outputof said amplifier frequency components related to movement of said oneor more organs, said feedback filter coupled as an input to a subtractorcircuit having as one input the output of said amplifier, saidsubtractor element subtracting said filtered signal from said output.152. The device of claim 20 where said at least one physiologicalfunction is heart rate or lung rate.
 153. A device employing ultrawideband radar return signals for extracting physiological data from oneor more organs or physiological processes of a patient at one or moredepths of interest within said patient, the device including a pulserepetition frequency generator for providing a pulse train coupled to atransmitter and a receiver, each of said transmitter and receivercoupled to one or more antennas, said transmitter transmitting pulsesignals to said patient and said receiver receiving return signals fromsaid patient, said receiver coupled to an analog to digital converterand further including dual channels and a sample and hold circuitcoupled to each channel, each said sample and hold circuits triggered bythe pulse repetition frequency generator, said circuits coupled tointegrator elements and including a range compensated gain circuitincluding blanking to eliminate undesired ones of said return signals.